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前面几章我们介绍了监督学习，包括从带标签的数据中学习的回归和分类算法。本章，我们讨论无监督学习算法，聚类（clustering）。聚类是用于找出不带标签数据的相似性的算法。我们将介绍K-Means聚类思想，解决一个图像压缩问题，然后对算法的效果进行评估。最后，我们把聚类和分类算法组合起来，解决一个半监督学习问题。









在第一章，机器学习基础中，我">
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<article class="post-text h-entry hentry postpage" itemscope="itemscope" itemtype="http://schema.org/Article"><header><h1 class="p-name entry-title" itemprop="headline name"><a href="#" class="u-url">6-clustering-with-k-means</a></h1>

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                    Tao Junjie
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            <p class="dateline"><a href="#" rel="bookmark"><time class="published dt-published" datetime="2015-06-29T20:29:49+08:00" itemprop="datePublished" title="2015-06-29 20:29">2015-06-29 20:29</time></a></p>
            
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<h2 id="K-Means聚类">K-Means聚类<a class="anchor-link" href="6-clustering-with-k-means.html#K-Means%E8%81%9A%E7%B1%BB">¶</a>
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<p>前面几章我们介绍了监督学习，包括从带标签的数据中学习的回归和分类算法。本章，我们讨论无监督学习算法，聚类（clustering）。聚类是用于找出不带标签数据的相似性的算法。我们将介绍K-Means聚类思想，解决一个图像压缩问题，然后对算法的效果进行评估。最后，我们把聚类和分类算法组合起来，解决一个半监督学习问题。</p>
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<p>在<em>第一章，机器学习基础</em>中，我们介绍过非监督学习的目的是从不带标签的训练数据中挖掘隐含的关系。聚类，或称为聚类分析（cluster analysis）是一种分组观察的方法，将更具相似性的样本归为一组，或一类（cluster），同组中的样本比其他组的样本更相似。与监督学习方法一样，我们用n维向量表示一个观测值。例如，假设你的训练数据如下图所示：</p>
<p><img src="mlslpic/6.1%20cluster.png" alt="cluster"></p>
<p>聚类算法可能会分成两组，用圆点和方块表示，如下图所示：</p>
<p><img src="mlslpic/6.2%20twocluster.png" alt="twocluster"></p>
<p>也可能分成四组，如下图所示：</p>
<p><img src="mlslpic/6.3%20fourcluster.png" alt="fourcluster"></p>
<p>聚类通常用于探索数据集。社交网络可以用聚类算法识别社区，然后向没有加入社区的用户进行推荐。在生物学领域，聚类用于找出有相似模式的基因组。推荐系统有时也利用聚类算法向用户推荐产品和媒体资源。在市场调查中，聚类算法用来做用户分组。下面，我们就用K-Means聚类算法来为一个数据集聚类。</p>

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<h3 id="K-Means聚类算法简介">K-Means聚类算法简介<a class="anchor-link" href="6-clustering-with-k-means.html#K-Means%E8%81%9A%E7%B1%BB%E7%AE%97%E6%B3%95%E7%AE%80%E4%BB%8B">¶</a>
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<p>由于具有出色的速度和良好的可扩展性，K-Means聚类算法算得上是最著名的聚类方法。K-Means算法是一个重复移动类中心点的过程，把类的中心点，也称重心（centroids），移动到其包含成员的平均位置，然后重新划分其内部成员。$K$是算法计算出的超参数，表示类的数量；K-Means可以自动分配样本到不同的类，但是不能决定究竟要分几个类。$K$必须是一个比训练集样本数小的正整数。有时，类的数量是由问题内容指定的。例如，一个鞋厂有三种新款式，它想知道每种新款式都有哪些潜在客户，于是它调研客户，然后从数据里找出三类。也有一些问题没有指定聚类的数量，最优的聚类数量是不确定的。后面我们会介绍一种启发式方法来估计最优聚类数量，称为肘部法则（Elbow Method）。</p>
<p>K-Means的参数是类的重心位置和其内部观测值的位置。与广义线性模型和决策树类似，K-Means参数的最优解也是以成本函数最小化为目标。K-Means成本函数公式如下：</p>
$$J = \sum_{k=1}^k\sum_{i \in {C_k}}{|x_i-\mu_k|}^2$$<p>$\mu_k$是第$k$个类的重心位置。成本函数是各个类畸变程度（distortions）之和。每个类的畸变程度等于该类重心与其内部成员位置距离的平方和。若类内部的成员彼此间越紧凑则类的畸变程度越小，反之，若类内部的成员彼此间越分散则类的畸变程度越大。求解成本函数最小化的参数就是一个重复配置每个类包含的观测值，并不断移动类重心的过程。首先，类的重心是随机确定的位置。实际上，重心位置等于随机选择的观测值的位置。每次迭代的时候，K-Means会把观测值分配到离它们最近的类，然后把重心移动到该类全部成员位置的平均值那里。让我们用如下表所示的训练数据来试验一下：</p>
<table>
<thead><tr>
<th style="text-align:center">样本</th>
<th style="text-align:center">X0</th>
<th style="text-align:center">X1</th>
</tr></thead>
<tbody>
<tr>
<td style="text-align:center">1</td>
<td style="text-align:center">7</td>
<td style="text-align:center">5</td>
</tr>
<tr>
<td style="text-align:center">2</td>
<td style="text-align:center">5</td>
<td style="text-align:center">7</td>
</tr>
<tr>
<td style="text-align:center">3</td>
<td style="text-align:center">7</td>
<td style="text-align:center">7</td>
</tr>
<tr>
<td style="text-align:center">4</td>
<td style="text-align:center">3</td>
<td style="text-align:center">3</td>
</tr>
<tr>
<td style="text-align:center">5</td>
<td style="text-align:center">4</td>
<td style="text-align:center">6</td>
</tr>
<tr>
<td style="text-align:center">6</td>
<td style="text-align:center">1</td>
<td style="text-align:center">4</td>
</tr>
<tr>
<td style="text-align:center">7</td>
<td style="text-align:center">0</td>
<td style="text-align:center">0</td>
</tr>
<tr>
<td style="text-align:center">8</td>
<td style="text-align:center">2</td>
<td style="text-align:center">2</td>
</tr>
<tr>
<td style="text-align:center">9</td>
<td style="text-align:center">8</td>
<td style="text-align:center">7</td>
</tr>
<tr>
<td style="text-align:center">10</td>
<td style="text-align:center">6</td>
<td style="text-align:center">8</td>
</tr>
<tr>
<td style="text-align:center">11</td>
<td style="text-align:center">5</td>
<td style="text-align:center">5</td>
</tr>
<tr>
<td style="text-align:center">12</td>
<td style="text-align:center">3</td>
<td style="text-align:center">7</td>
</tr>
</tbody>
</table>
<p>上表有两个解释变量，每个样本有两个特征。画图如下所示：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">matplotlib.font_manager</span> <span class="k">import</span> <span class="n">FontProperties</span>
<span class="n">font</span> <span class="o">=</span> <span class="n">FontProperties</span><span class="p">(</span><span class="n">fname</span><span class="o">=</span><span class="sa">r</span><span class="s2">"c:\windows\fonts\msyh.ttc"</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
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<div class="prompt input_prompt">In [66]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">X0</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">7</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
<span class="n">X1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">9</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X0</span><span class="p">,</span> <span class="n">X1</span><span class="p">,</span> <span class="s1">'k.'</span><span class="p">);</span>
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pn5K0HBGvl/Sbks41nqcJ20uSPiBpf0RcI2mXpHe0nOnFTHoxSzog6fsRsRIR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%0ArkJggg==">
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<p>假设K-Means初始化时，将第一个类的重心设置在第5个样本，第二个类的重心设置在第11个样本.那么我们可以把每个实例与两个重心的距离都计算出来，将其分配到最近的类里面。计算结果如下表所示：</p>
<table>
<thead><tr>
<th style="text-align:center">样本</th>
<th style="text-align:center">X0</th>
<th style="text-align:center">X1</th>
<th style="text-align:center">与C1距离</th>
<th style="text-align:center">与C2距离</th>
<th style="text-align:center">上次聚类结果</th>
<th style="text-align:center">新聚类结果</th>
<th style="text-align:center">是否改变</th>
</tr></thead>
<tbody>
<tr>
<td style="text-align:center">1</td>
<td style="text-align:center">7</td>
<td style="text-align:center">5</td>
<td style="text-align:center">3.16</td>
<td style="text-align:center">2.00</td>
<td style="text-align:center">None</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">2</td>
<td style="text-align:center">5</td>
<td style="text-align:center">7</td>
<td style="text-align:center">1.41</td>
<td style="text-align:center">2.00</td>
<td style="text-align:center">None</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">3</td>
<td style="text-align:center">7</td>
<td style="text-align:center">7</td>
<td style="text-align:center">3.16</td>
<td style="text-align:center">2.83</td>
<td style="text-align:center">None</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">4</td>
<td style="text-align:center">3</td>
<td style="text-align:center">3</td>
<td style="text-align:center">3.16</td>
<td style="text-align:center">2.83</td>
<td style="text-align:center">None</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">5</td>
<td style="text-align:center">4</td>
<td style="text-align:center">6</td>
<td style="text-align:center">0.00</td>
<td style="text-align:center">1.41</td>
<td style="text-align:center">None</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">6</td>
<td style="text-align:center">1</td>
<td style="text-align:center">4</td>
<td style="text-align:center">3.61</td>
<td style="text-align:center">4.12</td>
<td style="text-align:center">None</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">7</td>
<td style="text-align:center">0</td>
<td style="text-align:center">0</td>
<td style="text-align:center">7.21</td>
<td style="text-align:center">7.07</td>
<td style="text-align:center">None</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">8</td>
<td style="text-align:center">2</td>
<td style="text-align:center">2</td>
<td style="text-align:center">4.47</td>
<td style="text-align:center">4.24</td>
<td style="text-align:center">None</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">9</td>
<td style="text-align:center">8</td>
<td style="text-align:center">7</td>
<td style="text-align:center">4.12</td>
<td style="text-align:center">3.61</td>
<td style="text-align:center">None</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">10</td>
<td style="text-align:center">6</td>
<td style="text-align:center">8</td>
<td style="text-align:center">2.83</td>
<td style="text-align:center">3.16</td>
<td style="text-align:center">None</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">11</td>
<td style="text-align:center">5</td>
<td style="text-align:center">5</td>
<td style="text-align:center">1.41</td>
<td style="text-align:center">0.00</td>
<td style="text-align:center">None</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">12</td>
<td style="text-align:center">3</td>
<td style="text-align:center">7</td>
<td style="text-align:center">1.41</td>
<td style="text-align:center">2.83</td>
<td style="text-align:center">None</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">C1重心</td>
<td style="text-align:center">4</td>
<td style="text-align:center">6</td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
</tr>
<tr>
<td style="text-align:center">C2重心</td>
<td style="text-align:center">5</td>
<td style="text-align:center">5</td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
</tr>
</tbody>
</table>
<p>新的重心位置和初始聚类结果如下图所示。第一类用X表示，第二类用点表示。重心位置用稍大的点突出显示。</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">C1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">11</span><span class="p">]</span>
<span class="n">C2</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">))</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">C1</span><span class="p">))</span>
<span class="n">X0C1</span><span class="p">,</span> <span class="n">X1C1</span> <span class="o">=</span> <span class="n">X0</span><span class="p">[</span><span class="n">C1</span><span class="p">],</span> <span class="n">X1</span><span class="p">[</span><span class="n">C1</span><span class="p">]</span>
<span class="n">X0C2</span><span class="p">,</span> <span class="n">X1C2</span> <span class="o">=</span> <span class="n">X0</span><span class="p">[</span><span class="n">C2</span><span class="p">],</span> <span class="n">X1</span><span class="p">[</span><span class="n">C2</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'第一次迭代后聚类结果'</span><span class="p">,</span><span class="n">fontproperties</span><span class="o">=</span><span class="n">font</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">9</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X0C1</span><span class="p">,</span> <span class="n">X1C1</span><span class="p">,</span> <span class="s1">'rx'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X0C2</span><span class="p">,</span> <span class="n">X1C2</span><span class="p">,</span> <span class="s1">'g.'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="s1">'rx'</span><span class="p">,</span><span class="n">ms</span><span class="o">=</span><span class="mf">12.0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="s1">'g.'</span><span class="p">,</span><span class="n">ms</span><span class="o">=</span><span class="mf">12.0</span><span class="p">);</span>
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9edieMZt17wGT3P7ATqsoyyLgmcB+wBmZaylaNY259PMSu+QskoxZ3DXJ7Xd2%0AWkWPAn4v7mj/fxVwZM5CCsiir2oas1lhTgN+Mu62nwIfyFBLKZYB5wP7DPsYYyqeMZvNkfYFwNfT%0AjC/uBD7gF/5sjK/5Z2ZWmKF48a/0mVGXnEXiLBJnkZSeRTWN2cysFh5lmJllMBSjDDOzWlTTmEuf%0AGXXJWSTOInEWSelZVNOYzcxq4RmzmVkGnjGbmQ2Qahpz6TOjLjmLxFkkziIpPYtqGrOZWS08YzYz%0Ay8AzZjOzAVJNYy59ZtQlZ5E4i8RZJKVnMa3GLGmepGskfW6uCzIzG3bTmjFLeiPwVOBBEXHQuPuG%0Ae8YsHQBcTvR88Le0EFhKxMXZ6jKzos1oxizpkcD+wEeA4W3Ak7sceFfbjMea8rva283Mtth0Rhnv%0AA44D7pnjWmYk28yoWSmfRNOcR2ia8kmbraA7Vvr8rEvOInEWSelZzO93p6QDgVsi4pp+P4ikFcD6%0AdnMjsCYiVrf37QUw19s9tXRyvHttwynA9fvAoZfBkoBuj7/59hLyHr+YbWCJpGLq8XYZ22O6PH77%0A9eHtodfTR98Zs6STgZcBd9Ncjn1b4IKIeHnPY4Z7xgy944tTaJ5dZF0xm1n5ZuWaf5KeCbw5Ip4/%0A3Z0PhdSUm2Y8ftvMbAKz+QaTmb1NcA5lnBktpbcJp5nz0kz1FD8/65KzSJxFUnoWfWfMvSLiK8BX%0A5rCWwTTRKXFNc/apcmZ2n/izMszMMvBnZZiZDZBqGnPpM6MuOYvEWSTOIik9i2oas5lZLTxjNjPL%0AwDNmM7MBUk1jLn1m1CVnkTiLxFkkpWdRTWM2M6uFZ8xmZhl4xmxmNkCqacylz4y65CwSZ5E4i6T0%0ALKppzGZmtfCM2cwsA8+YzcwGSDWNufSZUZecReIsEmeRlJ5FNY3ZzKwWnjGbmWXQr3dO+womZtOl%0AUZ0JLALuAJbFcl/70GxLVDPKKH1m1KUCslgEPBPYDzgjZyEFZFEMZ5GUnkU1jdmKckf7/6uAI3MW%0AYjaIPGO2WadRLaRZKR/pMYbZxPr1TjdmM7MMhuINJqXPjLrkLBJnkTiLpPQsqmnMZma18CjDzCyD%0AoRhlmJnVoprGXPrMqEvOInEWibNISs+imsZsZlYLz5jNzDLwjNnMbIBU05hLnxl1yVkkziJxFknp%0AWVTTmM3MauEZs5lZBp4xm5kNkGoac+kzoy45i8RZJM4iKT2LKRuzpJ0krZK0VtL3JR3dRWFmZsNq%0AyhmzpO2B7SNijaRtgO8AL4yIde39njGbmW2hGc2YI+LmiFjTfv0HYB2ww+yWaGZmY7ZoxixpBHgK%0AcOVcFDMTpc+MuuQsEmeROIuk9CymfZXsdoyxEjimXTlbgTSqMzmE3TSqDfgK1WYDaVqNWdJWwAXA%0AORFx4QT3rwDWt5sbgTURsbq9by8Ab3ezzQ/ZjfuzGFgMnCHpwyXV13ke7W2l1JNzOyJWl1TPsG23%0AXx9OYz19TOfFPwEfB34TEW+Y4H6/+FcQjeoSYD+aK1Tv4xWzWZlm+gaTpcBhwN6Srmn/23dWK5wF%0Apc+MOrSMtazCTRnw70UvZ5GUnsWUo4yI+DoVvRGldrE8Nkp6Z5zvpmw2qPxZGWZmGfizMszMBkg1%0Ajbn0mVGXnEXiLBJnkZSeRTWN2cysFp4xm5ll4BmzmdkAqaYxlz4z6pKzSJxF4iyS0rOopjGbmdXC%0AM2Yzsww8YzYzGyDVNObSZ0ZdchaJs0icRVJ6FtU0ZjOzWnjGbGaWgWfMZmYDpJrGXPrMqEvOInEW%0AibNISs+imsZsZlYLz5jNzDLwjNnMbIBU05hLnxl1yVkkziJxFknpWVTTmM3MauEZs5lZBp4xm5kN%0AkGoac+kzoy45i8RZJM4iKT2LahqzmVktPGM2M8vAM2YzswFSTWMufWbUJWeROIvEWSSlZ1FNYzYz%0Aq4VnzGZmGXjGbGY2QKppzKXPjLrkLBJnkTiLpPQsqmnMZma18IzZzCwDz5jNzAZINY259JlRl5xF%0A4iwSZ5GUnkU1jdnMrBZTzpgl7QucCswDPhIR7xl3v2fMZmZbqF/v7NuYJc0DrgOeA9wEXAUcGhHr%0AprNzMzOb2Exe/NsN+ElErI+ITcB5wAtmu8DZUPrMqEvOInEWibNISs9iqsa8I3BDz/aN7W1F0ajO%0A5BBO1agu0agW5q7HzGwm5k9x/7ROcpa0Aljfbm4E1kTE6va+vQDmdPsQduMJLAYWs5bPSHpnp8cv%0AcHtMKfXk2h67rZR6cm5HxOqS6hm27fbrw2msp4+pZsy7A++IiH3b7ROBe3pfACxhxqxRXQLsRzMD%0A3yeWx8ac9ZiZTWUmM+ZvA4+TNCJpa+Bg4KLZLnAWLGMtq3BTBsqfn3XJWSTOIik9i76jjIi4W9Lr%0AgC/QnC53Vu8ZGaWI5bFR0jvjfDdlMxt8/qwMM7MM/FkZZmYDpJrGXPrMqEvOInEWibNISs+imsZs%0AZlYLz5jNzDLwjNnMbIBU05hLnxl1yVkkziJxFknpWVTTmM3MauEZs5lZBp4xm5kNkGoac+kzoy45%0Ai8RZJM4iKT2LahqzmVktPGM2M8vAM2YzswFSTWMufWbUJWeROIvEWSSlZ1FNYwaW5C6gIM4icRaJ%0As0iKzqKmxuyLsCbOInEWibNIis6ipsZsZlaFmhrzSO4CCjKSu4CCjOQuoCAjuQsoyEjuAvqZldPl%0AZqkWM7OhMtnpcjNuzGZmNrtqGmWYmVXBjdnMrDAD35gl7Svph5J+LOmE3PXkJGknSaskrZX0fUlH%0A564pJ0nzJF0j6XO5a8lJ0kJJKyWtk/QDSbvnrikXSSe2fx/XSvqEpPvnrmkiA92YJc0DPgjsCzwJ%0AOFTSE/NWldUm4A0R8bfA7sBRQ57HMcAPgGF/IeX9wCUR8URgF2Bd5nqykDQCvArYNSKeDMwDDslZ%0A02QGujEDuwE/iYj1EbEJOA94QeaasomImyNiTfv1H2j+AHfIW1Uekh4J7A98BBjaD9mS9GBgj4j4%0AKEBE3B0Rv8tcVi630SxeFkiaDywAbspb0sQGvTHvCNzQs31je9vQa1cHTwGuzFtJNu8DjgPuyV1I%0AZjsDv5b0MUlXS/ovSQtyF5VDRPwW+A/gF8AGYGNEfClvVRMb9MY87E9RJyRpG2AlcEy7ch4qkg4E%0AbomIaxji1XJrPrAr8KGI2BW4HXhL3pLykPRY4FiaN5fsAGwj6aVZi5rEoDfmm4CderZ3olk1Dy1J%0AWwEXAOdExIW568nkGcBBkq4HPgk8S9LZmWvK5Ubgxoi4qt1eSdOoh9HTgG9ExG8i4m7gMzS/K8UZ%0A9Mb8beBxkkYkbQ0cDFyUuaZsJAk4C/hBRJyau55cIuKtEbFTROxM8+LOlyPi5bnryiEibgZukLSo%0Avek5wNqMJeX0Q2B3SQ9s/1aeQ/PicHHm5y5gJiLibkmvA75A8wrrWRExlK84t5YChwHfk3RNe9uJ%0AEXFpxppKMOwjr9cD57aLl58Cr8xcTxYR8d32mdO3aV57uBo4M29VE/Nbss3MCjPoowwzs+q4MZuZ%0AFcaN2cysMG7MZmaFcWM2MyuMG7OZWWHcmM3MCuPGbGZWmP8HsWFyueDA9xYAAAAASUVORK5CYII=">
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<div class="prompt input_prompt">
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<p>现在我们重新计算两个类的重心，把重心移动到新位置，并重新计算各个样本与新重心的距离，并根据距离远近为样本重新归类。结果如下表所示：</p>
<table>
<thead><tr>
<th style="text-align:center">样本</th>
<th style="text-align:center">X0</th>
<th style="text-align:center">X1</th>
<th style="text-align:center">与C1距离</th>
<th style="text-align:center">与C2距离</th>
<th style="text-align:center">上次聚类结果</th>
<th style="text-align:center">新聚类结果</th>
<th style="text-align:center">是否改变</th>
</tr></thead>
<tbody>
<tr>
<td style="text-align:center">1</td>
<td style="text-align:center">7</td>
<td style="text-align:center">5</td>
<td style="text-align:center">3.49</td>
<td style="text-align:center">2.58</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">2</td>
<td style="text-align:center">5</td>
<td style="text-align:center">7</td>
<td style="text-align:center">1.34</td>
<td style="text-align:center">2.89</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">3</td>
<td style="text-align:center">7</td>
<td style="text-align:center">7</td>
<td style="text-align:center">3.26</td>
<td style="text-align:center">3.75</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">4</td>
<td style="text-align:center">3</td>
<td style="text-align:center">3</td>
<td style="text-align:center">3.49</td>
<td style="text-align:center">1.94</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">5</td>
<td style="text-align:center">4</td>
<td style="text-align:center">6</td>
<td style="text-align:center">0.45</td>
<td style="text-align:center">1.94</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">6</td>
<td style="text-align:center">1</td>
<td style="text-align:center">4</td>
<td style="text-align:center">3.69</td>
<td style="text-align:center">3.57</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">7</td>
<td style="text-align:center">0</td>
<td style="text-align:center">0</td>
<td style="text-align:center">7.44</td>
<td style="text-align:center">6.17</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">8</td>
<td style="text-align:center">2</td>
<td style="text-align:center">2</td>
<td style="text-align:center">4.75</td>
<td style="text-align:center">3.35</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">9</td>
<td style="text-align:center">8</td>
<td style="text-align:center">7</td>
<td style="text-align:center">4.24</td>
<td style="text-align:center">4.46</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">10</td>
<td style="text-align:center">6</td>
<td style="text-align:center">8</td>
<td style="text-align:center">2.72</td>
<td style="text-align:center">4.11</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">11</td>
<td style="text-align:center">5</td>
<td style="text-align:center">5</td>
<td style="text-align:center">1.84</td>
<td style="text-align:center">0.96</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">12</td>
<td style="text-align:center">3</td>
<td style="text-align:center">7</td>
<td style="text-align:center">1.00</td>
<td style="text-align:center">3.26</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">C1重心</td>
<td style="text-align:center">3.80</td>
<td style="text-align:center">6.40</td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
</tr>
<tr>
<td style="text-align:center">C2重心</td>
<td style="text-align:center">4.57</td>
<td style="text-align:center">4.14</td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
</tr>
</tbody>
</table>
<p>画图结果如下：</p>

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<div class="prompt input_prompt">In [73]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">C1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">11</span><span class="p">]</span>
<span class="n">C2</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">))</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">C1</span><span class="p">))</span>
<span class="n">X0C1</span><span class="p">,</span> <span class="n">X1C1</span> <span class="o">=</span> <span class="n">X0</span><span class="p">[</span><span class="n">C1</span><span class="p">],</span> <span class="n">X1</span><span class="p">[</span><span class="n">C1</span><span class="p">]</span>
<span class="n">X0C2</span><span class="p">,</span> <span class="n">X1C2</span> <span class="o">=</span> <span class="n">X0</span><span class="p">[</span><span class="n">C2</span><span class="p">],</span> <span class="n">X1</span><span class="p">[</span><span class="n">C2</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'第二次迭代后聚类结果'</span><span class="p">,</span><span class="n">fontproperties</span><span class="o">=</span><span class="n">font</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">9</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X0C1</span><span class="p">,</span> <span class="n">X1C1</span><span class="p">,</span> <span class="s1">'rx'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X0C2</span><span class="p">,</span> <span class="n">X1C2</span><span class="p">,</span> <span class="s1">'g.'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mf">3.8</span><span class="p">,</span><span class="mf">6.4</span><span class="p">,</span><span class="s1">'rx'</span><span class="p">,</span><span class="n">ms</span><span class="o">=</span><span class="mf">12.0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mf">4.57</span><span class="p">,</span><span class="mf">4.14</span><span class="p">,</span><span class="s1">'g.'</span><span class="p">,</span><span class="n">ms</span><span class="o">=</span><span class="mf">12.0</span><span class="p">);</span>
</pre></div>

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mZhl4xmxmNkSqacylz4y65CwSZ5E4i6T0LKppzGZmtfCM2cwsA8+YzcyGSDWN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%0AYdyYzcwK48ZsZlaY/wf4WkMNt4eqrgAAAABJRU5ErkJggg==">
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
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<p>我们再重复一次上面的做法，把重心移动到新位置，并重新计算各个样本与新重心的距离，并根据距离远近为样本重新归类。结果如下表所示：</p>
<table>
<thead><tr>
<th style="text-align:center">样本</th>
<th style="text-align:center">X0</th>
<th style="text-align:center">X1</th>
<th style="text-align:center">与C1距离</th>
<th style="text-align:center">与C2距离</th>
<th style="text-align:center">上次聚类结果</th>
<th style="text-align:center">新聚类结果</th>
<th style="text-align:center">是否改变</th>
</tr></thead>
<tbody>
<tr>
<td style="text-align:center">1</td>
<td style="text-align:center">7</td>
<td style="text-align:center">5</td>
<td style="text-align:center">2.50</td>
<td style="text-align:center">5.28</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">2</td>
<td style="text-align:center">5</td>
<td style="text-align:center">7</td>
<td style="text-align:center">0.50</td>
<td style="text-align:center">5.05</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">3</td>
<td style="text-align:center">7</td>
<td style="text-align:center">7</td>
<td style="text-align:center">1.50</td>
<td style="text-align:center">6.38</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">4</td>
<td style="text-align:center">3</td>
<td style="text-align:center">3</td>
<td style="text-align:center">4.72</td>
<td style="text-align:center">0.82</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">5</td>
<td style="text-align:center">4</td>
<td style="text-align:center">6</td>
<td style="text-align:center">1.80</td>
<td style="text-align:center">3.67</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">6</td>
<td style="text-align:center">1</td>
<td style="text-align:center">4</td>
<td style="text-align:center">5.41</td>
<td style="text-align:center">1.70</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">7</td>
<td style="text-align:center">0</td>
<td style="text-align:center">0</td>
<td style="text-align:center">8.90</td>
<td style="text-align:center">3.56</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">8</td>
<td style="text-align:center">2</td>
<td style="text-align:center">2</td>
<td style="text-align:center">6.10</td>
<td style="text-align:center">0.82</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">9</td>
<td style="text-align:center">8</td>
<td style="text-align:center">7</td>
<td style="text-align:center">2.50</td>
<td style="text-align:center">7.16</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">10</td>
<td style="text-align:center">6</td>
<td style="text-align:center">8</td>
<td style="text-align:center">1.12</td>
<td style="text-align:center">6.44</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">11</td>
<td style="text-align:center">5</td>
<td style="text-align:center">5</td>
<td style="text-align:center">2.06</td>
<td style="text-align:center">3.56</td>
<td style="text-align:center">C2</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">YES</td>
</tr>
<tr>
<td style="text-align:center">12</td>
<td style="text-align:center">3</td>
<td style="text-align:center">7</td>
<td style="text-align:center">2.50</td>
<td style="text-align:center">4.28</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">C1</td>
<td style="text-align:center">NO</td>
</tr>
<tr>
<td style="text-align:center">C1重心</td>
<td style="text-align:center">5.50</td>
<td style="text-align:center">7.00</td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
</tr>
<tr>
<td style="text-align:center">C2重心</td>
<td style="text-align:center">2.20</td>
<td style="text-align:center">2.80</td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
<td style="text-align:center"></td>
</tr>
</tbody>
</table>
<p>画图结果如下：</p>

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<div class="prompt input_prompt">In [76]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">C1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">11</span><span class="p">]</span>
<span class="n">C2</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">12</span><span class="p">))</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">C1</span><span class="p">))</span>
<span class="n">X0C1</span><span class="p">,</span> <span class="n">X1C1</span> <span class="o">=</span> <span class="n">X0</span><span class="p">[</span><span class="n">C1</span><span class="p">],</span> <span class="n">X1</span><span class="p">[</span><span class="n">C1</span><span class="p">]</span>
<span class="n">X0C2</span><span class="p">,</span> <span class="n">X1C2</span> <span class="o">=</span> <span class="n">X0</span><span class="p">[</span><span class="n">C2</span><span class="p">],</span> <span class="n">X1</span><span class="p">[</span><span class="n">C2</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'第三次迭代后聚类结果'</span><span class="p">,</span><span class="n">fontproperties</span><span class="o">=</span><span class="n">font</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">9</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X0C1</span><span class="p">,</span> <span class="n">X1C1</span><span class="p">,</span> <span class="s1">'rx'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X0C2</span><span class="p">,</span> <span class="n">X1C2</span><span class="p">,</span> <span class="s1">'g.'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mf">5.5</span><span class="p">,</span><span class="mf">7.0</span><span class="p">,</span><span class="s1">'rx'</span><span class="p">,</span><span class="n">ms</span><span class="o">=</span><span class="mf">12.0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="mf">2.2</span><span class="p">,</span><span class="mf">2.8</span><span class="p">,</span><span class="s1">'g.'</span><span class="p">,</span><span class="n">ms</span><span class="o">=</span><span class="mf">12.0</span><span class="p">);</span>
</pre></div>

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mHqO5GOw7cjekLJzFZvMow2w+NGccXLLJHLVpREsnvVhszZzFpDxjNjMrjGfM%0AQ8ZZJM4icRZJ6VlU05jNzGrhUYaZWQZDMcowM6tFNY259JlRl5xF4iwSZ5GUnkU1jdnMrBaeMZuZ%0AZeAZs5nZAKmmMZc+M+qSs0icReIsktKzqKYxm5nVwjNmM7MMPGM2Mxsg1TTm0mdGXXIWibNInEVS%0AehYzasySFki6StIX57sgM7NhN6MZs6TXA08FHhgRB064zzNmM7PNNKsZs6RHAPsBZwBuwGZm82wm%0Ao4z30Vyr6+55rmVWcs+MNKrTNao1GtVFGvX1zErhLBJnkZSexcJ+d0o6ALg5Iq7q90QkrQTG2sUN%0AwNqIWNPetwxgvpd7aulke/fY/gnsBDyTa4Hb+TywV8Z6dgY6ff6lLgM7SyqmHi+XsTyuy+233x/a%0AbnqMPvrOmCWdCLwMuAu4H7A1cF5EvLznMZ4xAxrVRcC+wBXA3rGi5/pmZmYTzMk1/yQ9E3hjRDxv%0ApisfJu344jTgCDdlM5vOXP6Byez+THAe5Z4ZxYrYECvioBKacu4sSuIsEmeRlJ5F3xlzr4j4OvD1%0AeazFzMzwZ2WYmWXhz8owMxsg1TTm0mdGXXIWibNInEVSehbVNGYzs1p4xmxmloFnzGZmA6Saxlz6%0AzKhLziJxFomzSErPoprGbGZWC8+Yzcwy8IzZzGyAVNOYS58ZdclZJM4icRZJ6VlU05jNzGrhGbOZ%0AWQaeMZuZDZBqGnPpM6MuOYvEWSTOIik9i2oas5lZLTxjNjPLoF/vnPEVTMxmSqM6HdgJuB14SQmX%0A2zIbJNWMMkqfGXWpgCx2Ap5Jc9Xw03IWUkAWxXAWSelZeI95SGhU+wGvBe4H3AmcEivionna3O3t%0Av1cAR8zTNsyq5RnzEGib8vuBx/Tc/DPgqPlozhrVYpo95SM8xjCbXL/e6cY8BDSqi4HnTnLXxbEi%0A9u26HjMbkj8wKX1m1KVJsrjfFA+9/zyXkp1fF4mzSErPoprGbH3dOcXtd3RahZnNSDWNOSLW5K6h%0AFJNkcQrNTLnXz4EPdFJQRn5dJM4iKT0Lz5iHRHsA8DU044s7gA/M41kZZjaNoTj4J2lZ6b8Fu+Is%0AEmeROIukhCyG4uCfmVktqtljNjMbJN5jNjMbINU05tLPS+ySs0icReIsktKzqKYxm5nVwjNmM7MM%0APGM2Mxsg1TTm0mdGXXIWibNInEVSehbTNmZJO0haLWmdpB9Kem0XhZmZDatpZ8yStgW2jYi1krYC%0Avge8ICLWt/d7xmxmtplmNWOOiJsiYm37/Z+A9cB2c1uimZmN26wZs6QR4CnAZfNRzGyUPjPqkrNI%0AnEXiLJLSs5jxNf/aMcYq4Kh2z9kKpFGdznJ21ahuxFeoNhtIM2rMkrYAzgM+FRHnT3L/SmCsXdwA%0ArB3/5Kbx30xe7maZH7Mr92UJsAQ4TdKHS6qv8zzY9JPEcteTczki1pRUz7Att98fSmOMPmZy8E/A%0AJ4DfRcTrJrnfB/8KolFdBOxLc4Xqvb3HbFam2f6ByVLgEGBPSVe1X/vMaYVzoPSZUYdewjpW46YM%0A+HXRy1kkpWcx7SgjIr5FRX+IUrtYERskvT3OdVM2G1T+rAwzswz8WRlmZgOkmsZc+syoS84icRaJ%0As0hKz6KaxmxmVgvPmM3MMvCM2cxsgFTTmEufGXXJWSTOInEWSelZVNOYzcxq4RmzmVkGnjGbmQ2Q%0Aahpz6TOjLjmLxFkkziIpPYtqGrOZWS08YzYzy8AzZjOzAVJNYy59ZtQlZ5E4i8RZJKVnUU1jNjOr%0AhWfMZmYZeMZsZjZAqmnMpc+MuuQsEmeROIuk9CyqacxmZrXwjNnMLAPPmM3MBkg1jbn0mVGXnEXi%0ALBJnkZSeRTWN2cysFp4xm5ll4BmzmdkAqaYxlz4z6pKzSJxF4iyS0rOopjGbmdXCM2Yzsww8YzYz%0AGyDVNObSZ0ZdchaJs0icRVJ6FtU0ZjOzWnjGbGaWgWfMZmYDpJrGXPrMqEvOInEWibNISs+imsZs%0AZlaLaWfMkvYBTgYWAGdExDsn3O8Zs5nZZurXO/s2ZkkLgGuAZwM3AFcAB0fE+pms3MzMJjebg3+7%0AAj+LiLGI2AicAzx/rgucC6XPjLrkLBJnkTiLpPQspmvM2wPX9Sxf395WFI3qdJZzskZ1kUa1OHc9%0AZmazsXCa+2d0krOklcBYu7gBWBsRa9r7lgHM6/JyduXxLAGWsI7PS3p7p9svcHlcKfXkWh6/rZR6%0Aci5HxJqS6hm25fb7Q2mM0cd0M+bdgBMiYp92+Xjg7t4DgCXMmDWqi4B9aWbge8eK2JCzHjOz6cxm%0Axvxd4LGSRiRtCRwEXDDXBc6Bl7CO1bgpA+XPz7rkLBJnkZSeRd9RRkTcJenVwJdoTpc7s/eMjFLE%0Aitgg6e1xrpuymQ0+f1aGmVkG/qwMM7MBUk1jLn1m1CVnkTiLxFkkpWdRTWM2M6uFZ8xmZhl4xmxm%0ANkCqacylz4y65CwSZ5E4i6T0LKppzGZmtfCM2cwsA8+YzcwGSDWNufSZUZecReIsEmeRlJ5FNY3Z%0AzKwWnjGbmWXgGbOZ2QCppjGXPjPqkrNInEXiLJLSs6imMQM75y6gIM4icRaJs0iKzqKmxuyLsCbO%0AInEWibNIis6ipsZsZlaFmhrzSO4CCjKSu4CCjOQuoCAjuQsoyEjuAvqZk9Pl5qgWM7OhMtXpcrNu%0AzGZmNrdqGmWYmVXBjdnMrDAD35gl7SPpx5J+Kum43PXkJGkHSaslrZP0Q0mvzV1TTpIWSLpK0hdz%0A15KTpMWSVklaL+lHknbLXVMuko5v/39cLenTku6bu6bJDHRjlrQA+CCwD/BE4GBJT8hbVVYbgddF%0AxD8AuwFHDnkeRwE/Aob9QMr7gYsi4gnAk4H1mevJQtII8Epgl4h4ErAAWJ6zpqkMdGMGdgV+FhFj%0AEbEROAd4fuaasomImyJibfv9n2j+A26Xt6o8JD0C2A84AxjaD9mS9CBg94j4GEBE3BURf8hcVi63%0A0uy8LJK0EFgE3JC3pMkNemPeHriuZ/n69rah1+4dPAW4LG8l2bwPOAa4O3chme0I/FbSxyVdKemj%0AkhblLiqHiPg98B7gV8CNwIaI+GreqiY36I152N+iTkrSVsAq4Kh2z3moSDoAuDkirmKI95ZbC4Fd%0AgA9FxC7AbcCb8paUh6RHA0fT/HHJdsBWkl6atagpDHpjvgHYoWd5B5q95qElaQvgPOBTEXF+7noy%0AeQZwoKRrgc8Ae0k6K3NNuVwPXB8RV7TLq2ga9TB6GvDtiPhdRNwFfJ7mtVKcQW/M3wUeK2lE0pbA%0AQcAFmWvKRpKAM4EfRcTJuevJJSLeHBE7RMSONAd3vhYRL89dVw4RcRNwnaSd2pueDazLWFJOPwZ2%0Ak3T/9v/Ks2kODhdnYe4CZiMi7pL0auBLNEdYz4yIoTzi3FoKHAL8QNJV7W3HR8TFGWsqwbCPvF4D%0AnN3uvPwcOCxzPVlExPfbd07fpTn2cCVwet6qJuc/yTYzK8ygjzLMzKrjxmxmVhg3ZjOzwrgxm5kV%0Axo3ZzKwwbsxmZoVxYzYzK4wbs5lZYf4fNaNSmEG58owAAAAASUVORK5CYII=">
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<p>再重复上面的方法就会发现类的重心不变了，K-Means会在条件满足的时候停止重复聚类过程。通常，条件是前后两次迭代的成本函数值的差达到了限定值，或者是前后两次迭代的重心位置变化达到了限定值。如果这些停止条件足够小，K-Means就能找到最优解。不过这个最优解不一定是全局最优解。</p>

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<h4 id="局部最优解">局部最优解<a class="anchor-link" href="6-clustering-with-k-means.html#%E5%B1%80%E9%83%A8%E6%9C%80%E4%BC%98%E8%A7%A3">¶</a>
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<p>前面介绍过K-Means的初始重心位置是随机选择的。有时，如果运气不好，随机选择的重心会导致K-Means陷入局部最优解。例如，K-Means初始重心位置如下图所示：</p>
<p><img src="mlslpic/6.4%20clusterlocaloptima.png" alt="clusterlocaloptima"></p>
<p>K-Means最终会得到一个局部最优解，如下图所示。这些类可能没有实际意义，而上面和下面两部分观测值可能是更有合理的聚类结果。为了避免局部最优解，K-Means通常初始时要重复运行十几次甚至上百次。每次重复时，它会随机的从不同的位置开始初始化。最后把最小的成本函数对应的重心位置作为初始化位置。</p>
<p><img src="mlslpic/6.5%20clusterlocaloptima2.png" alt="clusterlocaloptima2"></p>

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<h4 id="肘部法则">肘部法则<a class="anchor-link" href="6-clustering-with-k-means.html#%E8%82%98%E9%83%A8%E6%B3%95%E5%88%99">¶</a>
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<p>如果问题中没有指定$K$的值，可以通过肘部法则这一技术来估计聚类数量。肘部法则会把不同$K$值的成本函数值画出来。随着$K$值的增大，平均畸变程度会减小；每个类包含的样本数会减少，于是样本离其重心会更近。但是，随着$K$值继续增大，平均畸变程度的改善效果会不断减低。$K$值增大过程中，畸变程度的改善效果下降幅度最大的位置对应的$K$值就是肘部。下面让我们用肘部法则来确定最佳的$K$值。下图数据明显可分成两类：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">cluster1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">1.5</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">cluster2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mf">3.5</span><span class="p">,</span> <span class="mf">4.5</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">((</span><span class="n">cluster1</span><span class="p">,</span> <span class="n">cluster2</span><span class="p">))</span><span class="o">.</span><span class="n">T</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span><span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span><span class="s1">'k.'</span><span class="p">);</span>
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<p>我们计算$K$值从1到10对应的平均畸变程度：</p>

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<div class="prompt input_prompt">In [104]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="k">import</span> <span class="n">KMeans</span>
<span class="kn">from</span> <span class="nn">scipy.spatial.distance</span> <span class="k">import</span> <span class="n">cdist</span>
<span class="n">K</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">meandistortions</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">K</span><span class="p">:</span>
    <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">k</span><span class="p">)</span>
    <span class="n">kmeans</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
    <span class="n">meandistortions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">cdist</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">,</span> <span class="s1">'euclidean'</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span> <span class="o">/</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">K</span><span class="p">,</span> <span class="n">meandistortions</span><span class="p">,</span> <span class="s1">'bx-'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'平均畸变程度'</span><span class="p">,</span><span class="n">fontproperties</span><span class="o">=</span><span class="n">font</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'用肘部法则来确定最佳的K值'</span><span class="p">,</span><span class="n">fontproperties</span><span class="o">=</span><span class="n">font</span><span class="p">);</span>
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src="%0AAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xm4XFWZ7/Hvj5AEAkqAQCIQCAgYBERkaBqMnFZsZpxQ%0AtBVtriKNE84yqMT2clVudysiClcBEYXYotK0RIUIYVKRIcyEJgySMIQxCIQASd77x1pF9qnUOVXn%0AnKraVXV+n+epp/betWrvt+ok+6017LUVEZiZ2ei2RtkBmJlZ+ZwMzMzMycDMzJwMzMwMJwMzM8PJ%0AwMzMcDIwMzOcDKzDSTpU0rplx9EISX2SxtUpM0HSmk041qDHKZR7U2FZxXWzIicDaypJF0i6QdKf%0AJN0o6dG8/CdJCyUdJelhSZdLuqPqvftJ+nrVLjcCDiqUuSvv63ZJ35a0laQfStqkUGaipN9V7Xua%0ApMWSrsqPv0raW6vMlLRvjc/zXkkfrrG9L3+WoyT9k6RtgHOAI/K2XQf4ij4G/OsA391HJN2av5u5%0AhedbJb2/UG4M8MdiUpF0dY39bQscX9jUBxxRo9zywvJnJP1ekvL66ZKmSposaYqkK/LyZEkbD/AZ%0ArQuN+BeKWZUADo6IhyS9GpgZEYcDSDoxl/lNRBwp6VJJhwKb5u3Tga0kHQFsTEoCy/N7Pw2cBzwY%0AEW+WNBNYEBH3SjoVeL2kI4F9gDHAdElX5f3+B3AjMBuYCWwJ7J1f+1dgTo4bSZOACyvrwCRgjKR/%0ALnzGTwKLgXcBfwXuBk4BPgw8m49xT97fQcBpwEOF7wdJf8rrk4HjIuLn+bUolqvxDHAwcAnwUUnv%0Ay9t2KHzek4DxwMnAmnn7ScBhwD9Iui6XOz0izizE1Jc/w54REZLWAyYCuwHbAQI2AT6Sl1cA38B6%0AgpOBtYKqnqu37y/pUtKJ5Trgzrz9+VzmqohYIGk30i/p9YCTI+K7kt6RfxkfALxR0ueAyyNiNulk%0A/7V8EpsVEfu/fGBpi7z4auDvBwo8Ih6X9Hbg6xFxtKTDgHUi4qycdE6LiPl5nxsBxwJfAl4FnED6%0AP7U9cLykqcADOfbTan5RKck8X/h+/lL4Piq2r3x3+Rf7l4ATIuIy4Pt5+1URMaOw36NIJ+qfAEcB%0AewJbRsS0/Pq7SUmxUn4z4AzgoIj4W978AVLS+TPwcWAl6W/Rl+P5wkDfo3UfJwNrNgG/kvQisBaw%0AeeEX6+bA/wFmR8RHc0IYC7whIs7NJ6SpOREcCjwCHAe8FrhR0gF5P/sBf4yIFyVdCFwo6Z0RcXch%0AhoGMBV5kkH/7OSHcLemDwAukc/CHgMcKiWAi6QT7beBiYBvgn0nJ4RN5V2/Pn3k/SR8oHH9d4KnK%0A4YDZ+Rf+3wFrA3tVhbQ+MEPSDFKymJpj+m/SL3eAHQvf83XAXemjxEpJQWoSPk/S5hHxQP78Lxa+%0ArwuAT1W+w5xQT8ifZ23gUVJyqXy3HwQ2GOg7tO7jZGDNNh44MJ9QtwZOrNFMVDxZPwh8VtJ5Vdvv%0AAR6u2vfT+fn3wJGSdo2I65U6RScUTobjgFcX1h8CvpiX1wWeAybUCl7SSUCxk3USqdnpMWClpLcC%0A84AnSCfI7wFvzWVnA9NIyQrgdxHxNUnfAI6IiDMk7QR8OiJqtd3/F/CV/D2cAHyLVSd7gCuAs0h9%0AE0TEwYX3fjIiTi2s/wupdvIRYArwdeAG4D8lvTl/Ry/k4gEsBN5I+m4hNSn9klV/k1cBMwrrL9cq%0ArDc4GVizTQQukrSC2jWDb7KqmehVEfG8pD+QTqDLC/t5DJgFvJTXx5KahoiI5bl5aCZwOOkX+E0R%0AMUPSeFKzxusjYlFlZ4Vmos1ITTcb1Qo+Ik4ovOcQ0sl5TeDzEfGnwmtfA+az6uQ4jpQUvkLqcyDH%0ABam9fVdSM4yAAwt9BgAzImI5KZFWTrhjgD2AnUgn6C+Tag5HA5+vEfp7gFML6wGcFBE/yU1Giojb%0AJZ1DqsG8xKpkAKlj+TpJt0TEL4CzgXeS/oYAWwFvK3zezenfj2FdzsnAmm1CROwCMEAH8vrALyLi%0AMzkhAHw5Ipap/2ietYB5EfHJ/N5zSQmh4ilW/Wo+EjgkL38F+GFELJI0C/hSRPwVeBz4EamZaikp%0AKc0ndTj3k5uAvkZqy/934JXAYZIOBr4ZEX+LiBNzTBUbkjqKdyW18QPcnp8PyvuAdAK9uFbNgFRr%0AOTQvPwucDvwceCZ/Zy8CL+aBPkj6IqkzGfo3E/2g8lGqnomIH+T3HkPqBK9sf1bSu4DLJf1PRNxc%0AOU72X5W/RX7/52DQ5jjrMi0fWpqHpV2uNBTwNkmfqlGmT9LTkublx5dbHZc1X+7wvb+4qarIONJJ%0Arl/bfkQsy+sTSZ2UdQ6jS4ErgTMlTQOez81SbyP1L5yRRwVdDJwrSRHxHKmNfzlwB6njdXxEfCUi%0AriC1i4fSMNI5pBP2saST94qI+DRwDXBJPglD6ow+JS8/FBFHA78hjdz5Rt7f+qRRR49IOpb+Ca34%0AoSaRmq++AfwU2CK/71bSKKpfVr8nIk6OiBm54/jWynJEVJrcvpJrILU6erdiVbNb5P3dAXyK1Acz%0AqRge8Hal0V9zJM0BPlrrc1j3akfN4CXgMxFxk9LFQzdIujQiqkdMXBERh9R4v3WPw4F/K6yvAJbl%0AX5xfJLVdLwCOkfRnYBGApM+SRg2tDfxTfu9K4ABJO+T1qXlbRESljZ58gv1VXv0IuZmK1Mz0GLAE%0A+HhOGlsD74yIpyVdA+yRO6U/SepDOJWUzM7Nv8IrKifLiyXNZlUT0ykR8XOlYa4/ztueBf4R+BBp%0A2Or3SSf4C/J3cDawYf78Qfo/+Bfgd8C5pFrLJcC+wJtJ/ROvAz4t6d6IOL3w2Ss1AYBNC+uLgLnA%0AvxaaiSL34fyCVONZh1SLIiJevoAtD3H9ed4/pL/ZT/PLYwrHGwP8m6T3FDrurYup3Xc6y6M/To2I%0APxS29QGfK3aIWfeRND4iXqhTZlzViXakx1wXUjPHIK+vRao9PNes41YfY5DjvyoiqjvCW07SWICI%0AeKnGa039G1hvaGsyyL/OrgC2L/7nkbQ36dfdItLoks/nKquZmbVB2zqQ8y+0C4BjavyKupE0vnyp%0ApP1JozG2bVdsZmajXVtqBrnK+hvgtxHxnQbK3wfsEhFPVm33UDYzs2GIiEFHf7W8ZqDUC3UmcMdA%0AiUDSZODRPB/K7qQk9WStsvU+UCeQNDMiZpYdRz3dEGc3xAiOs9kcZ3M18kO6Hc1Ee5HmOLlF0ry8%0A7XjSRStExBmksdVHK82euBR4bxviMjOzrOXJICKups71DHkSr5oTeZmZWev5fgatMbfsABo0t+wA%0AGjC37AAaNLfsABo0t+wAGjS37AAaNLfsAJql7dcZjES6kLTz+wzMzDpJI+dO1wzMzMzJwMzMnAzM%0AzAwnAzMzw8nAzMxwMjAzM5wMzMwMJwMzM8PJwMzMcDIwMzOcDMzMDCcDMzPDycDMzHAyMDMznAzM%0AzAwnAzMzw8nAzMxwMjAzM5wMzMwMJwMzM8PJwMzMcDIwMzOcDMzMjC5NBhITJQ4sOw4zs17RdclA%0AYiJwEnBN2bGYmfUKRUTZMTRMUkB8HzghgiVlx2Nm1g0kRURo0DJdmAy2jOD+smMxM+sWjSSDrmsm%0AAr6Qm4rMzKxJujEZ/BA4yQnBzKx5ujEZ7AGcAOxVdiBmZr2iG5PBPhEsieDisgMxM+sV3diB/BSw%0AUQQryo7HzKwb9GoH8kPAG8oOwsysl3RjMpgDvLXsIMzMeknLk4GkqZIul3S7pNskfWqAct+VdLek%0AmyXtPMguLwX2aU20ZmajUztqBi8Bn4mI7UkjgT4uabtiAUkHAFtHxDbAR4EfDLK/K4HdJCa0KmAz%0As9Gm5ckgIh6JiJvy8rPAncAmVcUOAc7JZa4FJkqaXHt/PAPMA2a0LGgzs1GmrX0GkqYBOwPXVr20%0AKbCwsL4I2GyQXc3BTUVmZk2zZrsOJGld4ALgmFxDWK1I1XrNMa+SZsJOU+EtB0r/cXFEzG1upGZm%0A3U1SH9A3pPe04zoDSWOB3wC/jYjv1Hj9dGBuRMzK6/OBvSNicVW5iAhJrAk8DmwbwaMt/wBmZl2s%0AI64zkCTgTOCOWokguwj4YC6/B7CkOhEURbAcuAJ4S5PDNTMbldrRTLQX8AHgFknz8rbjgc0BIuKM%0AiJgt6QBJC4DngCMa2G9liOn5LYjZzGxU6brpKCpVHYnpwCXAFhG1+xfMzKxDmola6C5Sp/M2ZQdi%0AZtbtujYZ5NqAh5iamTVB1yaDzPMUmZk1Qdf2GaR1JgPzSVNaLy8vMjOzztXrfQZEsJh05fKuZcdi%0AZtbNujoZZJ7F1MxshHohGbjfwMxshLq6zyBtYx1gMTAlglpzHpmZjWo932cAEMFzwPV4Smszs2Hr%0A+mSQXYqbiszMhq1XkoEvPjMzG4FeSQY3AFMlppQdiJlZN+qJZJAvOLscT2ltZjYsPZEMMg8xNTMb%0Apl5KBpcC+0ir3T7TzMzq6KVksABYAUwvOxAzs27TM8kgT2ntqSnMzIahZ5JB5iGmZmbD0PXTUfR/%0AnY2Au0lTWr/UvsjMzDrXqJiOoiiCx4D7gN3KjsXMrJv0VDLIPMTUzGyIejEZuBPZzGyIeqrPIJVh%0AAmlK600ieKY9kZmZda5R12cAEMFS4C/A3mXHYmbWLXouGWQeYmpmNgS9mgx8fwMzsyHo1WQwD5gi%0AsUnZgZiZdYOeTAYRrCBNae2mIjOzBvRkMsg8xNTMrEG9nAzm4Cmtzcwa0rPJIIJ7gGXAa8uOxcys%0A0/VsMsg8NYWZWQN6PRm438DMrAE9Nx1F//JsCNwLTPKU1mY2Wo3K6SiKIniCdH+DPcqOxcyskw2a%0ADCQdLGk7SZ8e4PVPSlq33kEknSVpsaRbB3i9T9LTkublx5cbC78hnprCzKyOejWDrYANgAMrGyQd%0AKWmLvHoo8FwDxzkb2K9OmSsiYuf8+N8N7LNR7kQ2M6tjzQbKHA3sLOky4EFgCnCYpCOApdFAp0NE%0AXCVpWp1irboe4GpgR4n1Ini6RccwM+tq9WoGawCnk+b6OQK4K28/jvSL+4ImxRHAnpJuljRbUtOu%0ADYhgGfBnoK9Z+zQz6zUDJgNJ7wGOJ52oyc+VxzxgLHBdk+K4EZgaETsBpwIXNmm/FR5iamY2iMGa%0Aif4AfBPYCdgYOADYjNSc8x/AT4EPAF8caRAR8Uxh+beSvi9pg4h4srqspJmF1bkRMbeBQ8wBfjbS%0AOM3MuoGkPobYGjJgMoiIJyQtB14BjAPGAFcCWwN/iojzJc0dbrBFkiYDj0ZESNqddP3DaokgxzVz%0AGIe4CZgksVkEi0YQqplZx8s/kudW1iWdWO89jXQgXw3sEBGn5Z1uEBHn59cWSpoWEfcPtgNJ55Nu%0AQzlJ0kLgRFIzExFxBmlU0tE5+SwF3ttAXA2LYKXEZaSmoh83c99mZr1g0CuQJf0zcCTwYmUTqZ/h%0AceAq4BbgmohY1towX45nSFcg938vRwJ9Eby/yWGZmXW0Rs6dw5qOQtIUYH/SL/h3RcSzwwtxyMcd%0ASTKYBlwLTImge+bgMDMboRFPRyFpg8JjvKQP5pc2joizgQfblQhGKoL7gWeAHUsOxcys49S7zuBO%0A4DTSENJ9SNcaAJySn7dsUVyt4iGmZmY11EsGd0TE+0ijiOYAa0gaX3juNp6awsyshqHOWvo64HeF%0A5/WaHlFrXQbsJdGNiczMrGWGmgxuioh/KDx31Vw/ETwFzMdTWpuZ9VMvGawp6XLS7KUHFba/OED5%0AbnApbioyM+tnsLmJxgGX5xrAaaSreE+SdHJE7JuL/bj1ITad729gZlZlsOkoXpS0n6T7gX2B15J+%0AUV8m6cNtiq8V/ghsL7F+bjYyMxv16jUTPQIsI93AZkdgKrAd8CzpKuTHWxpdC0TwAnANntLazOxl%0A9eYmmk1qVhkL/Il0P+HDgPcDR0bE4taG1zKVIaa/LjsQM7NOUHc6CknfjIhjJU0C3h0RPyi89vfA%0ABhFxcYvjrBxv2NNR9N8POwG/iGDbJoRlZtbRRjwdRbaXpPWBi4H7q157PTBtWNGV61ZgosQWdUua%0AmY0CjV5n8Dbg8/nGM0skHZy370Ca/K2rRLASjyoyM3tZI8lgGrA9cIikN5P6DT6S76SzK+mWld3I%0AycDMLBuwA1nSGNK9DB4HZuXND5FGF32ANERzVkSsbHWQLTIH+KbEGrmmYGY2ag1WM9gIeCXphjZj%0ASImjUn58Xn+hpdG1UAQPAE+R5lkyMxvVBkwGEfFIRJwMbAC8A3g7MB14FfBL4D15WzfzLKZmZjTW%0AZ/BX0lQUsyLiD6RawmERcStws6SdWhlgi/n+BmZmND6a6ErgFElvBM6NiEfy9qvp7pPpXGBPibXK%0ADsTMrEyNJIPZEfEw8C5gz4j4auG1a4CHWxJZG0SwBLgN2LPsWMzMytRIMnhc0obAO4G7JY3L90Me%0AGxEPRMR5LY6x1TzE1MxGvUamo7gU+ENefRuwKC+PB9aPiBmtC2+1WJoyHUX/ffIm4N8j2K2Z+zUz%0A6xSNnDsHnahO0uHA2qSLzn4E3Fd4z23AmU2Is2x/Bl4jsUEET5YdjJlZGeo1Ez0HbE261qDic8A6%0AwKHA11sUV9tE8CKpI/zNZcdiZlaWQZNBRPyKVAO4Dtgvbx4DTCBdc3BRS6NrHw8xNbNRrZEO5IuA%0AHwIrSFccf5XUXPTTFsbVbr74zMxGtXp9BjuQRhHtCowjDcF8ML+8PXAPqebQ7W4D1pHYKoJ7yw7G%0AzKzd6tUM1gKCdBXyQ6Tawf15/YmI6IVEQASBh5ia2SjWSDPRE8ACUhJ4PC/fDawvaUrrQms79xuY%0A2ag16HUG+WKzwSajuzoi7mp6VAPH0/TrDFbtm02Bm4GNPaW1mfWSEV9nEBFPSLoXmETqPF4GPA8s%0AJU3/fF+TYi1dBA9KPArsDNxQdjxmZu00aDLIPkaa0G08qQ9hIql5aQvSBWkHtSq4ElT6DZwMzGxU%0AaSQZLIuI0yorkq4G3hoRz0v6s6Q1uvhuZ9XmAJ8CvlV2IGZm7dToFNZI2kTSb4H/BD5Y2d5DiQBS%0ADejvJNYuOxAzs3YaMBlIGiPpKmAHSeOA7wLHA+cAH87Fvtj6ENsngr8BtwBvLDsWM7N2Guy2lytI%0A/QGnA5cAl0fEvIh4GpgnaZeIuLKRg0g6S9JiSbcOUua7ku6WdLOknYf4OZrJQ0zNbNSpNzfR0xFx%0ABvBr0rUFFc9ExFA6Wc9m1dxGq5F0ALB1RGwDfBT4wRD23Wy++MzMRp1Bk4GkfSXtC3ykan3Xofx6%0Aj4irSENRB3IIqfmJiLgWmChpcqP7b7Jrga0lJpV0fDOztqvXgTwd2I40S+l2eX06cBXwv5oYx6bA%0AwsL6ImCzJu6/YRG8RLrns6e0NrNRo14yuCUivgNcHBGnRMQpwHYR8RXSPQ2aqfrquMFvwdZal+JZ%0ATM1sFKl3ncHJkr4K3FjYtgtARDSzZvAgMLWwvhmrZkftR9LMwurciJjbxDgq5gCflVCexM7MrGtI%0A6gP6hvKeRi46+yPwPUl7kq5GboWLgE8AsyTtASyJiMW1CkbEzBbFUHQnMBZ4NWliPjOzrpF/JM+t%0ArEs6sd576iaDPJT0cEmfAP5+OIFJOh/YG5gkaSFwIulkS0ScERGzJR0gaQHpVptHDOc4zRJBSC+P%0AKnIyMLOeV2/W0vtI9zkutud/ubAtIuKslkbYP56WzVq6+rE4HHh7BO9qx/HMzFqlkXNnvQ5kkSaj%0AWys/1q7a1svTNswB/kFiTNmBmJm1Wr2awXURsVtefjdwF3BmZVu7tbNmkI7HbcAREVzXrmOamTVb%0AM2oGSBov6Uzg3cAjzQquS3iIqZmNCo3MWvom4KqIeE9EPNrqgDqMp6Yws1Gh3miiz9WYjG403fjl%0ASmCWxIQIlpYdjJlZqwzaZ9Bp2t1nkI7JlcBJEfy+ncc1M2uWpvQZGHNwv4GZ9Tgng/p8fwMz63lu%0AJqp7TNYEHge2jWC0daCbWQ9wM1ETRLCcNMfHW0oOxcysZZwMGuMhpmbW05wMGnMp8FZptXsumJn1%0ABCeDxvwPaU6mbcoOxMysFZwMGpBvcOMhpmbWs5wMGuchpmbWszy0tOFjMxmYD2yURxiZmXUFDy1t%0AoggWAw8Au5Ydi5lZszkZDI2HmJpZT3IyGBrf38DMepL7DIZ0fNYBFgNTIni2rDjMzIbCfQZNFsFz%0AwPWkG/6YmfUMJ4Oh8xBTM+s5TgZD54vPzKznOBkM3fXAZhJTyg7EzKxZnAyGKIIVwOV4Smsz6yFO%0ABsPjIaZm1lOcDIZnDrCPp7Q2s17hZDA8C4DlwPSyAzEzawYng2EoTGntIaZm1hOcDIbPQ0zNrGd4%0AOophktiI1Fw0KYKXyo7HzGwgno6ihSJ4DLgX2L3sWMzMRsrJYGQ8NYWZ9QQng5FxJ7KZ9QT3GYyA%0AxNrAo8AmETxTdjxmZrW4z6DFInge+Auwd9mxmJmNRFuSgaT9JM2XdLekL9V4vU/S05Lm5ceX2xFX%0Ak3hqCjPremu2+gCSxgDfI7WtPwhcJ+miiLizqugVEXFIq+NpgTnAOWUHYWY2Eu2oGewOLIiI+yPi%0AJWAW8LYa5TqmL2CI5gFTJDYtOxAzs+FqRzLYFFhYWF+UtxUFsKekmyXNlvTaNsTVFHlK68vwlNZm%0A1sVa3kxEOtHXcyMwNSKWStofuBDYtlZBSTMLq3MjYu6IIxy5yhDTn5QdiJmZpD6gb0jvafXQUkl7%0AADMjYr+8fhywMiK+Nch77gN2iYgnq7Z31NDSComtgKuBTfMkdmZmHaNThpZeD2wjaZqkccBhwEXF%0AApImS1Je3p2UpJ5cfVedKYJ7gWVA1zRvmZkVtbyZKCKWS/oE8HtgDHBmRNwp6aj8+hnAocDRkpYD%0AS4H3tjquFqgMMb297EDMzIbKVyA3icS7gQ9FcFDZsZiZFTVy7nQyaBKJDUmzmG4UwYtlx2NmVtEp%0AfQajQgRPAHcDf1d2LGZmQ+Vk0CQSBwJXUZiaQmJi3m5m1tGcDJrnGmArYF9IiQA4KW83M+to7jNo%0AIonJpKutPwG8ETgmgqfKjcrMRjt3IJdA4ljgG8DDwLqkoaa3ALdWHhF0zTUUZtb9Gjl3tmM6ilEj%0ANw1NBbYEvgCcDGwB7Ai8DvgnYAeJv1FIDqRkMT+CF8qI28zMNYMmKfQRnBDBkur1QjmREsTrSEmi%0Akii2JA1NrSSHSqL4q6e4MLORcDNRG+VRQ9dUnfgnAntFcHED718LmM6q5FBJFK+gfy2i0tTkvggz%0Aa4iTQQ/IF7PtSP9axPbAElavRcyvd8HbSJOWmXUfJ4MeJbEGMI3VaxHTgAWs3h+xsNLU1Ghzlpn1%0ADieDUUZibWA7+tcidgTWBm5jVXK4D3gXKQl8AScCs57mZGAASExi9VpEJUn8EbgJmA/cmZ8fdKe1%0AWe9wMrCaCk1DPwOOJSWELUi1iunAOqSkUHzcCSzwJHxm3cfJwFbTSJ+BxPrAa1iVHKbn5c2BB1hV%0Ag3i5NuFmJrPO5WRgqxnJaCKJ8cCrWZUcphcez9E/SVQSxaIIVrbgo5hZg5wMrC3yhXSb0r8WUVle%0AD/gfVq9N3D3QFdce/mrWXE4GVjqJ9UhNTtWJYktgETWanICVePirWdM4GVjHkhjLqian6kTxAulG%0AQesB/wXsDnwfuB94FHjMHdlmjXMysK6Tm5ymkBLDnsDXgQtJI5w2zo+NgGdJiaHyeKxqvfh40v0W%0ANpp51lLrOvn6hoclniddGFeZAbY42mkNYCL9k0NleTtg78L6xsB6Ek+wepIYKIE808h1Fu7bsF7i%0AmoF1nGZPmZGbpDakf4KofhQTylgGr2lUXltGupHRse7bsE7mZiLrSmX/4paYQP/kUCthFB+QbmY0%0AHpiXlx8b6BHBc63+DGZFTgZmLZb7OHYEbgYOBVYAk0iJY6BHkBLD4wySNAqPp0cyPUjZydXK5z4D%0As9ZbDziKGn0bteTksQ4DJ4pta2wbLzWcOB5j9Q7za4CTpNWb3ZrxBVhvcM3AbJjaNR14vvFRvdpG%0A8fFK4En61zyeJk1U+FtgBnBefv25wmNpcbndw3ddg2kdNxOZtVCnnrwKHebVSWJr4BhgFqmpap38%0AmDDAcjBAomCQJNLga0sjeKkq7q6410an/t0H42RgZkC/E+3/pfHmrLGsSgyDJY3BXhus3ApWTxTL%0ASFOb3ElqevszqVazND+er3oeaPnl5whWDO9bG/C76YqkVeRkYGYdefLKyWYctZPEVsCPgU+TTuhr%0A59crzxPqbKt+/SXqJ42hbhsD/AtwKnAkcBzweCfdB6RYg3EyMLOuatYYag2mgf2JNOS3XgIZzrZX%0Aku4D8kzeLvrXchp9brTs80NJNv0HCugpJwMz6wqdWIMZSK2kRTppF2s5zX4eT6qVDCXRrATeAprh%0AZGBmXaFbajBlJa08DUulVjKUJDIFdLiTgZlZE3VL0oJi4tLHnAzMzEYh9xmYmdmQRxOt0Z6gtJ+k%0A+ZLulvSlAcp8N79+s6Sd2xGXmVmviuDiofRhtDwZSBoDfA/YD3gt8D5J21WVOQDYOiK2AT4K/KDV%0AcbWSpL6yY2hEN8TZDTGC42w2x9l+7agZ7A4siIj7I+Il0qXwb6sqcwhwDkBEXAtMlDS5DbG1Sl/Z%0AATSor+wAGtBXdgAN6is7gAb1lR1Ag/rKDqBBfWUH0CztSAabAgsL64vytnplNmtxXGZmlrUjGTTa%0AQ13dudE9PdtmZl2u5aOJJO0BzIyI/fL6ccDKiPhWoczpwNyImJXX5wN7R8Tiqn05QZiZDUMn3Nzm%0AemAbSdOAh4DDgPdVlbmIdC/ZWTl5LKlOBFD/w5iZ2fC0PBlExHJJnwB+T5rp78yIuFPSUfn1MyJi%0AtqQDJC0gzadxRKvjMjOzVbrqojMzM2uNtlx0NlKSzpK0WNKtZccyEElTJV0u6XZJt0n6VNkx1SJp%0ALUnXSrpJ0h2SvlF2TIORNEbSPEn/XXYsA5F0v6Rbcpx/KTuegUiaKOkCSXfmv/0eZcdUTdJr8vdY%0AeTzdif/3gnTyAAAEDklEQVSXJB2X/6/fKuk8SePLjqkWScfkGG+TdMygZbuhZiBpBvAs8JOI2LHs%0AeGqRNAWYEhE3SVoXuAF4e0TcWXJoq5E0ISKWSloTuBr4fERcXXZctUj6LLAL8IqIOKTseGqRdB+w%0AS0Q8WXYsg5F0DnBFRJyV//brRMTTZcc1EElrAA8Cu0fEwnrl2yX3f14GbBcRL0j6OTA7Is4pNbAq%0AknYAzgd2I93g53fAv0TEPbXKd0XNICKuAp4qO47BRMQjEXFTXn6WdNu+TcqNqraIWJoXx5H6cTry%0AJCZpM+AA4EesPvS403R0fJLWA2ZExFmQ+vI6ORFk+wD3dFIiyP5GOrlOyEl1AilpdZrpwLURsSwi%0AVgBXAO8cqHBXJINuk3857AxcW24ktUlaQ9JNwGLg8oi4o+yYBvBt0o1DVpYdSB0BzJF0vaQjyw5m%0AAFsCj0k6W9KNkn4oaULZQdXxXuC8soOolmuA/w48QBohuSQi5pQbVU23ATMkbZD/1gcyyMW8TgZN%0AlpuILgCOyTWEjhMRKyPi9aR/GG/qxPlVJB0EPBoR8+jwX93AXhGxM7A/8PHcrNlp1gTeAHw/It5A%0AGrV3bLkhDUzSOOBg4Bdlx1JN0qtJ92eeRqr9ryvp/aUGVUNEzAe+BVwC/BaYxyA/rJwMmkjSWOCX%0AwE8j4sKy46knNxNcDOxadiw17AkcktvjzwfeLOknJcdUU0Q8nJ8fA35Nmo+r0ywCFkXEdXn9AlJy%0A6FT7Azfk77TT7Ar8MSKeiIjlwK9I/147TkScFRG7RsTewBLgroHKOhk0iSQBZwJ3RMR3yo5nIJIm%0ASZqYl9cG3kr6xdBRIuL4iJgaEVuSmgsui4gPlh1XNUkTJL0iL68D/CPQcaPeIuIRYKGkbfOmfYDb%0ASwypnveRfgR0ovnAHpLWzv/v9wE6sqlV0sb5eXPgHQzS7NaOK5BHTNL5wN7AhpIWAl+NiLNLDqva%0AXsAHgFskVU6ux0XE70qMqZZXAefkkRprAOdGxB9KjqkRnTrsbTLw63ROYE3gZxFxSbkhDeiTwM9y%0AE8w9dOjFnTmp7gN0ZP9LRNyca6nXk5pdbgT+X7lRDegCSRuSOrw/FhF/G6hgVwwtNTOz1nIzkZmZ%0AORmYmZmTgZmZ4WRgZmY4GZiZGU4GZmaGk4HZiEia1slTq5s1ysnAzMycDMyaRdJWeUbQXcqOxWyo%0AumI6CrNOJ+k1pLl0PhQRbjayruNkYDZyGwMXAu/I0wabdR03E5mN3BLgr0An3sfArCGuGZiN3Iuk%0A2wn+XtKzEdGpUy+bDcjJwGzkIiKW5ruzXSrpmYj4TdlBmQ2Fp7A2MzP3GZiZmZOBmZnhZGBmZjgZ%0AmJkZTgZmZoaTgZmZ4WRgZmY4GZiZGfD/AWlQCHi1tHqDAAAAAElFTkSuQmCC">
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<p>从图中可以看出，$K$值从1到2时，平均畸变程度变化最大。超过2以后，平均畸变程度变化显著降低。因此肘部就是$K=2$。下面我们再用肘部法则来确定3个类的最佳的K值：</p>

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<div class="prompt input_prompt">In [136]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">x1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">9</span><span class="p">])</span>
<span class="n">x2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x1</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span><span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span><span class="s1">'k.'</span><span class="p">);</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="k">import</span> <span class="n">KMeans</span>
<span class="kn">from</span> <span class="nn">scipy.spatial.distance</span> <span class="k">import</span> <span class="n">cdist</span>
<span class="n">K</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">meandistortions</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">K</span><span class="p">:</span>
    <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">k</span><span class="p">)</span>
    <span class="n">kmeans</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
    <span class="n">meandistortions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">cdist</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">,</span> <span class="s1">'euclidean'</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span> <span class="o">/</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">K</span><span class="p">,</span> <span class="n">meandistortions</span><span class="p">,</span> <span class="s1">'bx-'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'平均畸变程度'</span><span class="p">,</span><span class="n">fontproperties</span><span class="o">=</span><span class="n">font</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'用肘部法则来确定最佳的K值'</span><span class="p">,</span><span class="n">fontproperties</span><span class="o">=</span><span class="n">font</span><span class="p">);</span>
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src="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%0AZBARESQZREQE8P8BmtMYK7/TBJoAAAAASUVORK5CYII=">
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<p>从图中可以看出，$K$值从1到3时，平均畸变程度变化最大。超过3以后，平均畸变程度变化显著降低。因此肘部就是$K=3$。</p>

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<h3 id="聚类效果评估">聚类效果评估<a class="anchor-link" href="6-clustering-with-k-means.html#%E8%81%9A%E7%B1%BB%E6%95%88%E6%9E%9C%E8%AF%84%E4%BC%B0">¶</a>
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<p>我们把机器学习定义为对系统的设计和学习，通过对经验数据的学习，将任务效果的不断改善作为一个度量标准。K-Means是一种非监督学习，没有标签和其他信息来比较聚类结果。但是，我们还是有一些指标可以评估算法的性能。我们已经介绍过类的畸变程度的度量方法。本节为将介绍另一种聚类算法效果评估方法称为轮廓系数（Silhouette Coefficient）。轮廓系数是类的密集与分散程度的评价指标。它会随着类的规模增大而增大。彼此相距很远，本身很密集的类，其轮廓系数较大，彼此集中，本身很大的类，其轮廓系数较小。轮廓系数是通过所有样本计算出来的，计算每个样本分数的均值，计算公式如下：</p>
$$s = \frac {ba} {max(a,b)}$$<p>$a$是每一个类中样本彼此距离的均值，$b$是一个类中样本与其最近的那个类的所有样本的距离的均值。下面的例子运行四次K-Means，从一个数据集中分别创建2，3，4，8个类，然后分别计算它们的轮廓系数。</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="k">import</span> <span class="n">KMeans</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">metrics</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span> 
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">x1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">9</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">9</span><span class="p">])</span>
<span class="n">x2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x1</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">])</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'样本'</span><span class="p">,</span><span class="n">fontproperties</span><span class="o">=</span><span class="n">font</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2</span><span class="p">)</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'b'</span><span class="p">,</span> <span class="s1">'g'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">,</span> <span class="s1">'m'</span><span class="p">,</span> <span class="s1">'y'</span><span class="p">,</span> <span class="s1">'k'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">]</span>
<span class="n">markers</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'o'</span><span class="p">,</span> <span class="s1">'s'</span><span class="p">,</span> <span class="s1">'D'</span><span class="p">,</span> <span class="s1">'v'</span><span class="p">,</span> <span class="s1">'^'</span><span class="p">,</span> <span class="s1">'p'</span><span class="p">,</span> <span class="s1">'*'</span><span class="p">,</span> <span class="s1">'+'</span><span class="p">]</span>
<span class="n">tests</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">]</span>
<span class="n">subplot_counter</span> <span class="o">=</span> <span class="mi">1</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tests</span><span class="p">:</span>
    <span class="n">subplot_counter</span> <span class="o">+=</span> <span class="mi">1</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">subplot_counter</span><span class="p">)</span>
    <span class="n">kmeans_model</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">t</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">kmeans_model</span><span class="o">.</span><span class="n">labels_</span><span class="p">):</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x1</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">x2</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">l</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="n">markers</span><span class="p">[</span><span class="n">l</span><span class="p">],</span><span class="n">ls</span><span class="o">=</span><span class="s1">'None'</span><span class="p">)</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">])</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">])</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'K = </span><span class="si">%s</span><span class="s1">, 轮廓系数 = </span><span class="si">%.03f</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">metrics</span><span class="o">.</span><span class="n">silhouette_score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">kmeans_model</span><span class="o">.</span><span class="n">labels_</span><span class="p">,</span><span class="n">metric</span><span class="o">=</span><span class="s1">'euclidean'</span><span class="p">)),</span><span class="n">fontproperties</span><span class="o">=</span><span class="n">font</span><span class="p">)</span>
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<pre>d:\programfiles\Miniconda3\lib\site-packages\numpy\core\_methods.py:59: RuntimeWarning: Mean of empty slice.
  warnings.warn("Mean of empty slice.", RuntimeWarning)
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+A7xYUSxmlodz2SyTcWWeJGkv4PGIuF1S1wDbzW1Z7I6I7jKvZzaWFDnTlTkM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">
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<p>很显然，这个数据集包括三个类。在$K=3$的时候轮廓系数是最大的。在$K=8$的时候，每个类的样本不仅彼此很接近，而且与其他类的样本也非常接近，因此这时轮廓系数是最小的。</p>

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<h3 id="图像量化">图像量化<a class="anchor-link" href="6-clustering-with-k-means.html#%E5%9B%BE%E5%83%8F%E9%87%8F%E5%8C%96">¶</a>
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<p>前面我们用聚类算法探索了结构化数据集。现在我们用它来解决一个新问题。图像量化（image quantization）是一种将图像中相似颜色替换成同样颜色的有损压缩方法。图像量化会减少图像的存储空间，由于表示不同颜色的字节减少了。下面的例子中，我们将用聚类方法从一张图片中找出包含图片大多数颜色的压缩颜色调色板（palette），然后我们用这个压缩颜色调色板重新生成图片。这个例子需要用<code>mahotas</code>图像处理库，可以通过<code>pip install mahotas</code>安装：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="k">import</span> <span class="n">KMeans</span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="k">import</span> <span class="n">shuffle</span>
<span class="kn">import</span> <span class="nn">mahotas</span> <span class="k">as</span> <span class="nn">mh</span>
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<p>首先，读入图片，然后将图片矩阵展开成一个行向量。</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">original_img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mh</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s1">'mlslpic</span><span class="se">\6</span><span class="s1">.6 tree.png'</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span> <span class="o">/</span> <span class="mi">255</span>
<span class="n">original_dimensions</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">original_img</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">depth</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">original_img</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">image_flattened</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">original_img</span><span class="p">,</span> <span class="p">(</span><span class="n">width</span> <span class="o">*</span> <span class="n">height</span><span class="p">,</span> <span class="n">depth</span><span class="p">))</span>
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<p>然后我们用K-Means算法在随机选择1000个颜色样本中建立64个类。每个类都可能是压缩调色板中的一种颜色。</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">image_array_sample</span> <span class="o">=</span> <span class="n">shuffle</span><span class="p">(</span><span class="n">image_flattened</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)[:</span><span class="mi">1000</span><span class="p">]</span>
<span class="n">estimator</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">image_array_sample</span><span class="p">)</span>
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<pre>KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=64, n_init=10,
    n_jobs=1, precompute_distances='auto', random_state=0, tol=0.0001,
    verbose=0)</pre>
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<p>之后，我们为原始图片的每个像素进行类的分配。</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cluster_assignments</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">image_flattened</span><span class="p">)</span>
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<p>最后，我们建立通过压缩调色板和类分配结果创建压缩后的图片：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">compressed_palette</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">cluster_centers_</span>
<span class="n">compressed_img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">compressed_palette</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
<span class="n">label_idx</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">width</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">height</span><span class="p">):</span>
        <span class="n">compressed_img</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">compressed_palette</span><span class="p">[</span><span class="n">cluster_assignments</span><span class="p">[</span><span class="n">label_idx</span><span class="p">]]</span>
        <span class="n">label_idx</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">122</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Original Image'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">original_img</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">121</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Compressed Image'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">compressed_img</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>

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Xu0SDfR68tM4kLnng4gqqWa/kUguiyQSljGH2e1DBcMT21atEk4DJ/g47mzs8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%0A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<h3 id="通过聚类学习特征">通过聚类学习特征<a class="anchor-link" href="6-clustering-with-k-means.html#%E9%80%9A%E8%BF%87%E8%81%9A%E7%B1%BB%E5%AD%A6%E4%B9%A0%E7%89%B9%E5%BE%81">¶</a>
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<p>在下面的例子中，我们将聚类和分类组合起来研究一个半监督学习问题。你将对不带标签的数据进行聚类，获得一些特征，然后用这些特征来建立一个监督方法分类器。</p>
<p>假设你有一只猫和一条狗。再假设你买了一个智能手机，表面上看是用来给人打电话的，其实你只是用来给猫和狗拍照。你的照片拍得很棒，因此你觉得你的朋友和同事一定会喜欢它们。而你知道一部分人只想看猫的照片，一部分人只想看狗的照片，但是要把这些照片分类太麻烦了。下面我们就做一个半监督学习系统来分别猫和狗的照片。</p>
<p>回想一下<em>第三章，特征抽取与处理</em>的内容，有一个原始的方法来给图片分类，是用图片的像素密度值或亮度值作为解释变量。和我们前面进行文本处理时的高维向量不同，图片的特征向量不是稀疏的。另外，这个方法对图片的亮度，尺寸，旋转的变化都十分敏感。在<em>第三章，特征抽取与处理</em>里面，我们还介绍了SIFT和SURF描述器，用来描述图片的兴趣点，这类方法对图片的亮度，尺寸，旋转变化都不敏感。在下面的例子中，我们将用聚类算法处理这些描述器来学习图片的特征。每个元素将被编码成从图片中抽取的，被分配到同一个类的描述器的数量。这种方法有时也称为视觉词袋（bag-of-features）表示法，由于这个类的集合与词袋模型里的词汇表类似。我们将使用<a href="https://www.kaggle.com/c/dogs-vs-cats/data">Kaggle's Dogs vs. Cats competition</a>里面的1000张猫图片和1000张狗图片数据。注意，图片有不同的尺寸；由于我们的特征向量不用像素表示，所有我们也不需要将所有图片都缩放成同样的尺寸。我们将训练其中60的图片，然后用剩下的40图片来测试：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">mahotas</span> <span class="k">as</span> <span class="nn">mh</span>
<span class="kn">from</span> <span class="nn">mahotas.features</span> <span class="k">import</span> <span class="n">surf</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="k">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="k">import</span> <span class="n">MiniBatchKMeans</span>
<span class="kn">import</span> <span class="nn">glob</span>
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<p>首先，我们加载图片，转换成灰度图，再抽取SURF描述器。SURF描述器与其他类似的特征相比，可以更快的被提取，但是从2000张图片中抽取描述器依然是很费时间的。</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">all_instance_filenames</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">all_instance_targets</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="s1">'cats-and-dogs-img/*.jpg'</span><span class="p">):</span>
    <span class="n">target</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="s1">'cat'</span> <span class="ow">in</span> <span class="n">f</span> <span class="k">else</span> <span class="mi">0</span>
    <span class="n">all_instance_filenames</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
    <span class="n">all_instance_targets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
<span class="n">surf_features</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">counter</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">all_instance_filenames</span><span class="p">:</span>
    <span class="nb">print</span><span class="p">(</span><span class="s1">'Reading image:'</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">mh</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">f</span><span class="p">,</span> <span class="n">as_grey</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">surf_features</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">surf</span><span class="o">.</span><span class="n">surf</span><span class="p">(</span><span class="n">image</span><span class="p">)[:,</span> <span class="mi">5</span><span class="p">:])</span>
    
<span class="n">train_len</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">all_instance_filenames</span><span class="p">)</span> <span class="o">*</span> <span class="o">.</span><span class="mi">60</span><span class="p">)</span>
<span class="n">X_train_surf_features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">surf_features</span><span class="p">[:</span><span class="n">train_len</span><span class="p">])</span>
<span class="n">X_test_surf_feautres</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">surf_features</span><span class="p">[</span><span class="n">train_len</span><span class="p">:])</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">all_instance_targets</span><span class="p">[:</span><span class="n">train_len</span><span class="p">]</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">all_instance_targets</span><span class="p">[</span><span class="n">train_len</span><span class="p">:]</span>
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<p>然后我们把抽取的描述器分成300个类。用<code>MiniBatchKMeans</code>类实现，它是K-Means算法的变种，每次迭代都随机抽取样本。由于每次迭代它只计算这些被随机抽取的一小部分样本与重心的距离，因此<code>MiniBatchKMeans</code>可以更快的聚类，但是它的畸变程度会更大。实际上，计算结果差不多：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n_clusters</span> <span class="o">=</span> <span class="mi">300</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Clustering'</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_train_surf_features</span><span class="p">),</span> <span class="s1">'features'</span><span class="p">)</span>
<span class="n">estimator</span> <span class="o">=</span> <span class="n">MiniBatchKMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">n_clusters</span><span class="p">)</span>
<span class="n">estimator</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train_surf_features</span><span class="p">)</span>
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<p>之后我们为训练集和测试集构建特征向量。我们找出每一个SURF描述器的类，用Numpy的<code>binCount()</code>进行计数。下面的代码为每个样本生成一个300维的特征向量：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">X_train</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">surf_features</span><span class="p">[:</span><span class="n">train_len</span><span class="p">]:</span>
    <span class="n">clusters</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">instance</span><span class="p">)</span>
    <span class="n">features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">clusters</span><span class="p">)</span>
    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">features</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">n_clusters</span><span class="p">:</span>
    <span class="n">features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_clusterslen</span><span class="p">(</span><span class="n">features</span><span class="p">))))</span>
    <span class="n">X_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
    
<span class="n">X_test</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">surf_features</span><span class="p">[</span><span class="n">train_len</span><span class="p">:]:</span>
    <span class="n">clusters</span> <span class="o">=</span> <span class="n">estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">instance</span><span class="p">)</span>
    <span class="n">features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">clusters</span><span class="p">)</span>
    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">features</span><span class="p">)</span> <span class="o">&lt;</span> <span class="n">n_clusters</span><span class="p">:</span>
    <span class="n">features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_clusterslen</span><span class="p">(</span><span class="n">features</span><span class="p">))))</span>
    <span class="n">X_test</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
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<p>最后，我们在特征向量和目标上训练一个逻辑回归分类器，然后估计它的精确率，召回率和准确率：</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">clf</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">C</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="s1">'l2'</span><span class="p">)</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Precision: '</span><span class="p">,</span> <span class="n">precision_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Recall: '</span><span class="p">,</span> <span class="n">recall_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Accuracy: '</span><span class="p">,</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">))</span>
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<p>半监督学习系统仅仅使用像素密度作为特征向量，就获得了比逻辑回归分类器更好的精确率和召回率。而且，我们的特征向量只有300维，比100x100像素图片的10000维要小得多。</p>

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<h3 id="总结">总结<a class="anchor-link" href="6-clustering-with-k-means.html#%E6%80%BB%E7%BB%93">¶</a>
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<p>本章，我们介绍了我们的第一个无监督学习方法：聚类。聚类是用来探索无标签数据的结构的。我们介绍了K-Means聚类算法，重复将样本分配的类里面，不断的更新类的重心位置。虽然K-Means是无监督学习方法，其效果依然是可以度量的；用畸变程度和轮廓系数可以评估聚类效果。我们用K-Means研究了两个问题。第一个问题是图像量化，一种用单一颜色表示一组相似颜色的图像压缩技术。我们还用K-Means研究了半监督图像分类问题的特征。</p>
<p>下一章，我们将介绍另一种无监督学习任务——降维（dimensionality reduction）。和我们前面介绍过的半监督猫和狗图像分类问题类似，降维算法可以在尽量保留信息完整性的同时，降低解释变量集合的维度。</p>

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